Abstract

Rapid urbanization is a global phenomenon that is reshaping the urban environment all over the world. This process is associated with significant socio-economic and demographic transformations, such as gentrification, but also the proliferation of deprived and vulnerable urban neighbourhoods. Studying this phenomenon is often hindered by the lack of updated spatial information, preventing a global understanding of the ongoing transformations and the potential design of effective interventions.

GeoAI technologies such as the combination of remote sensing data with machine learning offer the opportunity to systematically monitor these rapid developments, providing relevant information for better understanding urbanization processes in different parts of the world. Advances in deep learning can largely contribute to mapping urban land cover, land use, and identifying informal and vulnerable areas. Automated techniques to analyze temporal image series can reveal vital information about urban growth patterns and morphological changes.